Comparison of Backbones for Semantic Segmentation Network

Abstract

Abstract As for the classification network that is constantly emerging with each passing day, different classification network as the backbone of the semantic segmentation network may show different performance. This paper selected the road extraction data set of CVPR DeepGlobe, and compared the performance differences of VGG-16 as the backbone of Unet, ResNet34, ResNet101 and Xception as the backbone of AD-LinkNet. When VGG-16 is used as the backbone of the semantic segmentation network, it performs better in the face of long and wide road extraction. As the backbone of the semantic segmentation network, ResNet has a higher ability to extract small roads. When Xception is used as the backbone of the semantic segmentation network, it not only retains the characteristics of ResNet34, but also can effectively deal with the complex situation of extracting target covered by occlusions.